Bibliographic record
Abstract
Climate extremes are costly environmental hazards, which create social stressors due to impacts on all human uses of land and water resources. Humans are adaptable, but their capacity for adaptation is constrained by various social factors, including social inequality. This research project asked: “How do gender and other social factors shape the experience of flooding for individuals in a rural agricultural community?” A case study approach was used to develop an in-depth understanding of one rural area’s unique experience of climate hazards. The research focused on the rural community of Maple Creek, Saskatchewan, and its surrounding area. A total of 21 participants were interviewed about their experience of flooding. Gender was one factor that shaped experience. Socioeconomic status and age were also determinants of how successfully individuals negotiated the flood and how easily they recovered. Women participants were more likely to have a low income and socio-economic status, demonstrating the intersection of these social aspects. This project endeavours to add to the growing understanding of the complex interaction between social factors of individuals and communities by examining how gender and other social factors influence how people experience flooding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".